Image-level hemorrhage detection refers to the localization of the hemorrhage, or its signs, within the selected images (e.g., hyperdense regions, spots, asymmetries). Recall that in this work, we train learners on binary classification tasks either locally (on images for the strong learner \(h\) ), or globally (on examinations for the MIL learner \(H\) ) rather than on a segmentation task. In particular, the RSNA dataset does not provide ground-truth segmentations of the bleeds. In this context, one can attempt to localize the signs of hemorrhage by explaining the models' predictions and find their most important features (i.e., pixels) in order to bridge classification with detection. To this end, we use and compare two machine learning explainability methods [1]}, [2]}: \((i)\)  Grad-CAM [3]}Grad-CAM is available at https://github.com/jacobgil/pytorch-grad-cam., which is a saliency method based on sensitivity analysis and very popular in radiology [4]}, [5]}, [6]}, and \((ii)\)  h-Shap [7]}, an efficient hierarchical extension to Shapley-based image explanations that provably retains several of the theoretical benefits of game theoretic explanations (see sec:imagelevelhshap for details). To our knowledge, this is the first time explanation methods have been used to explain the predictions of a bag-level [8]} MIL classifier at the pixel-level [9]}. Even though the saliency maps produced by explanation methods provide a weaker sense of localization, they allow users to interpret a model's prediction and investigate their complex mechanisms.
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